We run the Word2Vec [1]} tool on the NYT corpus to initialize the word embeddings. The character embeddings and relation embeddings are initialized randomly. All embeddings are updated during training. We set the word embedding dimension \(d_w=300\) , relation embedding dimension \(d_r=300\) , character embedding dimension \(d_c=50\) , and character-based word feature dimension \(d_f=50\) . To extract the character-based word feature vector, we set the CNN filter width at 3 and the maximum length of a word at 10. The hidden dimension \(d_h\)  of the decoder LSTM cell is set at 300 and the hidden dimension of the forward and the backward LSTM of the encoder is set at 150. The hidden dimension of the forward and backward LSTM of the pointer networks is set at \(d_p=300\) . The model is trained with mini-batch size of 32 and the network parameters are optimized using Adam [2]}. Dropout layers with a dropout rate fixed at \(0.3\)  are used in our network to avoid overfitting.
